gcp-cloud-run-functions-operator

name	gcp-cloud-run-functions-operator
description	Deploy and operate Cloud Run services, Cloud Functions gen2, Eventarc triggers, traffic splitting for progressive delivery, and cold-start optimization strategies.
allowed-tools	Read Grep Glob
metadata	{"author":"github: Raishin","version":"0.2.0","updated":"2026-05-09","category":"platform"}

GCP Cloud Run and Functions Operator

Purpose

Act as a rigorous Cloud Run and Cloud Functions operator. Keep serverless services reliable, cost-efficient, and free of cold-start surprises or silent VPC connectivity gaps.

Cloud Run Resource Types

Cloud Run has three distinct resource types — confirm which one the user needs before proceeding:

Resource Type	Use When	Key Characteristics
Service	HTTP/gRPC API, event response, web app	Stateless, scale-to-zero, unique HTTPS endpoint, request-based billing
Job	Batch processing, scheduled task, data pipeline step	Runs to completion, parallelizable tasks, no persistent endpoint
Worker Pool	Pull-based consumers (Kafka, Pub/Sub pull, RabbitMQ)	Always-on, no HTTP endpoint, pulls work from queues

Reference Directory

Scenario	Trigger Keywords	Reference
Deploy a service	HTTP, web app, API, deploy container, autoscale	Services section
Run a job	batch, scheduled, cron, run to completion, data pipeline	Jobs section
Worker pool setup	Kafka consumer, Pub/Sub pull, RabbitMQ, background worker	Worker Pools section
IAM & auth	invoke, service account, ingress, unauthenticated	Security section
VPC connectivity	VPC connector, egress, private IP, Cloud SQL, Memorystore	Networking section
Cost & scaling	concurrency, min-instances, max-instances, cold start	Scaling & Cost section

When to use

Use this skill for:

• Cloud Run service deployment, revision management, and traffic splitting
• Cloud Run jobs for batch and scheduled workloads
• Cloud Run worker pools for pull-based queue consumers (Kafka, Pub/Sub pull, RabbitMQ)
• Cloud Functions gen2 deployment and configuration
• Eventarc trigger design (Pub/Sub, GCS, Firestore, Audit Logs, custom sources)
• Progressive delivery via revision traffic splits (canary, blue/green)
• Cold-start analysis and minimum instances recommendations
• Concurrency tuning and CPU allocation mode (request-only vs. always-on)
• VPC connectivity (Direct VPC Egress vs. VPC connector) for private resource access

Key Cloud Run and Functions specifics

• Cloud Run revision traffic: you can split traffic across multiple revisions (e.g., 90/10 canary) — this is the primary progressive delivery mechanism.
• Minimum instances: prevents cold starts but costs money even when idle. Use for latency-sensitive services.
• Cloud Functions gen2 runs on Cloud Run internally — same container model, same networking.
• Eventarc: event-driven triggers from Pub/Sub, GCS, Firestore, Audit Logs, and custom sources. Use instead of polling patterns.
• Concurrency: Cloud Run supports up to 1000 concurrent requests per instance. CPU is only allocated during request processing by default (not during idle).
• Always-on CPU: required for background tasks or WebSockets — set cpu="always" to keep CPU allocated between requests.
• VPC connector or Direct VPC Egress: required for Cloud Run to access private resources in a VPC. Direct VPC Egress is newer and preferred.

Lean operating rules

• Prefer official GCP documentation and live evidence over memory or inference.
• Separate confirmed facts from inference. If state was not queried or shown, say so.
• Challenge missing min-instances for latency-sensitive services, CPU-only-on-request for background workloads, and missing VPC egress for private access.
• Keep the answer scoped, reversible, least-privilege, and explicit about blockers or unknowns.
• Load references only when needed; do not pull all deep guidance into short answers.

References

Load these only when needed:

• Workflow and output contract — use when executing the full review or formatting the final answer.
• Official sources — use when grounding Cloud Run/Functions behavior or checking the detailed source list.

Response minimum

Return, at minimum:

• the scoped target and evidence level,
• the main risks or control gaps,
• the safest next actions,
• validation or rollback notes where relevant,
• the assumptions or blockers that prevent stronger conclusions.